WO2019115755A1 - Nouvel entérotype à faible nombre de cellules associé à une inflammation - Google Patents

Nouvel entérotype à faible nombre de cellules associé à une inflammation Download PDF

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WO2019115755A1
WO2019115755A1 PCT/EP2018/084920 EP2018084920W WO2019115755A1 WO 2019115755 A1 WO2019115755 A1 WO 2019115755A1 EP 2018084920 W EP2018084920 W EP 2018084920W WO 2019115755 A1 WO2019115755 A1 WO 2019115755A1
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subject
stool sample
genus
bacteroides
gut
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PCT/EP2018/084920
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English (en)
Inventor
Jeroen RAES
Gwen FALONY
Mireia VALLES-COLOMER
Lindsay DEVOLDER
Gunter KATHAGEN
Severine Vermeire
Sara VIEIRA-SILVA
João GUEDELHA SABINO
Marie D'HOOGHE
Jacques DE KEYSER
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Vib Vzw
Katholieke Universiteit Leuven, K.U.Leuven R&D
Vrije Universiteit Brussel
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Application filed by Vib Vzw, Katholieke Universiteit Leuven, K.U.Leuven R&D, Vrije Universiteit Brussel filed Critical Vib Vzw
Priority to US16/771,856 priority Critical patent/US11649508B2/en
Priority to EP18819105.0A priority patent/EP3724358A1/fr
Publication of WO2019115755A1 publication Critical patent/WO2019115755A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification

Definitions

  • the present invention relates to the field of inflammation-associated disorders or conditions, more particularly to gut inflammation and inflammation associated with primary sclerosing cholangitis, spondyloarthritis or multiple sclerosis.
  • Provided herein are means and methods to diagnose and treat or reduce the severity of inflammation-associated disorders or conditions in a subject in need thereof.
  • the human body is home to several microbes including bacteria, archaea, viruses and fungi.
  • Different microbial communities thrive in the different body habitats (e.g. mouth, nose, gut, skin, vagina).
  • the richness and complexity of these microbial communities varies according to the body habitats, being higher in the sites linked to the gastrointestinal tract. These communities are relatively stable over time in a healthy individual, in contrast with inter-individual variation which is high among healthy individuals (The Human Microbiome Project Consortium 2012 Nature 486:207-214).
  • the gut is by far the richest microbial habitat in the human body.
  • These intestinal symbionts co-exist with the host in a mutualistic, commensal or parasitic relationship.
  • Bacteroidesl Bacteroidesl (Bl) and Bacteroides2 (B2) based on cell densities (Vandeputte et al 2017 Nature doi:10.1038/nature24460). Perturbation of the gut microbiota, or dysbiosis, has been the subject of extensive research in several diseases such as asthma (Fujimura et al 2016 Nat Med 22:1187-1191), obesity (Turnbaugh et al 2009 Nature 457:480-484) and inflammatory bowel diseases (IBD) (Machiels et al 2014 Gut 63:1275-1283; Joossens et al 2011 Gut 60:631-637).
  • asthma Frejimura et al 2016 Nat Med 22:1187-1191
  • obesity Teurnbaugh et al 2009 Nature 457:480-484
  • IBD inflammatory bowel diseases
  • IBD Inflammatory bowel disease
  • CD Crohn's disease
  • UC ulcerative colitis
  • Patients with IBD suffer from a panoply of symptoms including diarrhea, abdominal pain, loss of weight, fever, fatigue, malaise and anorexia. In children, growth failure can also be observed.
  • IBD is also associated with extra-intestinal manifestations. Between 6-47% of the patients with IBD suffer at least from one extra-intestinal manifestation, being dermatological (e.g. pyoderma gangrenosum, erythema nodosum), ophthalmological (e.g.
  • C-reactive protein is a serum marker of systemic inflammation with lower sensitivity for intestinal inflammation.
  • fecal calprotectin a fecal marker for intestinal inflammation
  • MS multiple sclerosis
  • PSC primary sclerosing cholangitis
  • inflammatory bowel disease 16S rRNA gene sequencing was performed with lllumina MiSeq technology.
  • Flow cytometry was used to count the bacterial load of each sample.
  • Relative and quantitative microbiota profiling were compared in this cohort.
  • QMP quantitative microbiota profiling
  • the Bacteroides2 enterotype is also associated with systemic and intestinal inflammation, lower microbial diversity and lower microbial richness, suggesting that the Bacteroides2 enterotype may depict a vulnerable microbial community associated with disease or pre disease status. Moreover, Faecalibacterium was negatively correlated with increased fecal calprotectin, while the genera Fusobacterium, Veillonella, Escherichia, Shigella and Streptococcus increased with increasing fecal calprotectin. However, in stool samples from the PSC/IBC cohort we observed a striking co-exclusion between Fusobacterium and Veillonella. Intestinal inflammation is associated with reduced fecal cell counts. We disclose here that the Bacteroides2 enterotype represents a disease-prone intestinal microbiota composition. Therapeutic strategies that increase microbial load and microbial resilience may prevent development of intestinal inflammation.
  • the detection of the relative fraction of the Bacteroides genus can be replaced or supplemented by the detection of the relative fraction of the Faecalibacterium genus, wherein a low relative fraction of the Faecalibacterium genus within the stool sample of the subject to be diagnosed together with a microbial cell count of said sample that is lower than the microbial cell count of a stool sample of a healthy subject (and optionally by a low relative fraction of the Bacteroides genus within the stool sample of the subject to be diagnosed) is indicative for said subject to have or to develop an inflammatory disorder.
  • the Bacteroides and/or Faecalibacterium genus is determined using microbiota phylogenetic profiling, wherein said phylogenetic profiling is performed using PCR, RT-PCR, qPCR, multiplex PCR, high-throughput sequencing, metatranscriptomic sequencing, identification of strain-specific markers and/or 16S rRNA analysis.
  • said stool sample of said subject used to diagnose said subject with an inflammatory disease is further characterized by a lower bacterial diversity compared to a stool sample of a healthy subject.
  • the inflammatory disorders for which said application provides diagnostic methods are gut inflammatory disorders or inflammatory associated with primary sclerosing cholangitis, multiple sclerosis or spondyloarthritis.
  • said gut inflammatory disorder is selected from the list consisting of Crohn's disease, irritable bowel syndrome, inflammatory bowel disease (IBD), ulcerative colitis, celiac disease, and gut inflammation associated with primary sclerosing cholangitis, multiple sclerosis or spondyloarthritis.
  • the distribution of samples along the first axis of the PCoA separates healthy controls and the different patient groups: IBD (UC, CD), PSC without IBD (PSC) and PSC with concomitant IBD (PSC-UC, PSC-CD).
  • FIG. 1 Distribution of CRP according to enterotype.
  • CD Crohn's disease
  • CRP C-reactive protein
  • PSC primary sclerosing cholangitis
  • UC ulcerative colitis. Control (red); PSC, primary sclerosing cholangitis (yellow); PSC-CD (green); PSC-UC (blue); CD, Crohn's disease (purple); UC, ulcerative colitis (pink).
  • Standard amplicon sequencing and metagenomic analyses of the fecal microbiota quantify microbial taxa and metabolic pathways as fractions of the sample sequence library generated (Zhernakova et al 2016 Science 352: 565-569; Falony et al 2016 Science 352: 560-564). While such relative approaches do allow detection of disease-associated microbiome variation and identification of associated markers, they are limited in their ability to dissect the interplay between microbiota and host health (Valles- Colomer et al 2016 J Crohns Colitis 10:735-746; Satinsky et al 2013 Methods Enzymol 531: 237-250).
  • microbiome profiling a recently developed quantitative microbiome profiling (QMP) method to study gut microbiota variation in patients with different forms of inflammatory disorders.
  • QMP quantitative microbiome profiling
  • the QMP method is a combination of microbiome profiling and flow cytometric enumeration of microbial cells, addressing the challenges inherent to the quantitative analysis of a highly dense and complex microbiota embedded in a semi-solid matrix (Vandeputte et al 2017 Nature).
  • a method of detecting an inflammatory disorder in a subject comprising detecting whether a stool sample of said subject has a low microbial cell count and has a high relative fraction of the Bacteroides genus and/or a low relative fraction of the Faecalibacterium genus. Also a method of diagnosing an inflammatory disorder in a patient is provided, said method comprising determining whether a stool sample of said patient has a low microbial cell count and has a high relative fraction of the Bacteroides genus and/or a low relative fraction of the Faecalibacterium genus.
  • a method for measuring the probability of a subject developing or having an inflammatory disorder comprising measuring the microbial cell count of a stool sample of said subject, measuring the abundance of at least 10, at least 50 or at least 100 bacterial genera comprising the Bacteroides genus in said stool sample and determining the probability of the subject developing or having an inflammatory disorder, wherein a low microbial cell count and a high relative abundance of the Bacteroides genus in said stool sample indicates a high probability of the subject developing an inflammatory disorder.
  • said high relative abundance of the Bacteroides genus means that the Bacteroides genus belongs to the 25%, 10%, 5%, 3%, 2% or 1% most abundant genera in said stool sample of said subject.
  • a method for measuring the probability of a subject developing or having an inflammatory disorder comprising measuring the microbial cell count of a stool sample of said subject, measuring the abundance of at least 10, at least 50 or at least 100 bacterial genera comprising the Faecalibacterium genus in said stool sample and determining the probability of the subject developing or having an inflammatory disorder, wherein a low microbial cell count and a low relative abundance of the Faecalibacterium genus in said stool sample indicates a high probability of the subject developing an inflammatory disorder.
  • said low relative abundance of the Faecalibacterium genus means that the Faecalibacterium genus belongs to the 25%, 10%, 5%, 3%, 2% or 1% least abundant genera in said stool sample of said subject.
  • a method for measuring the probability of a subject developing or having an inflammatory disorder comprising measuring the microbial cell count of a stool sample of said subject, measuring the abundance of at least 10, at least 50 or at least 100 bacterial genera comprising the Bacteroides and Faecalibacterium genera in said stool sample and determining the probability of the subject developing or having an inflammatory disorder, wherein a low microbial cell count and a high relative abundance of the Bacteroides genus together with a low relative abundance of the Faecalibacterium genus in said stool sample indicates a high probability of the subject developing an inflammatory disorder.
  • said high relative abundance of the Bacteroides genus means that the Bacteroides genus belongs to the 10% most abundant genera in said stool sample of said subject and said low relative abundance of the Faecalibacterium genus means that the Faecalibacterium genus belongs to the 10% least abundant genera in said stool sample of said subject and said low microbial cell count is less than 1.5x1o 11 cell per gram of fresh stool.
  • the microbial cell count of a fecal or stool sample should be determined and the relative fraction or relative abundance of the Bacteroides genus and/or Faecalibacterium genus should be determined in view of a plurality of detectable and/or measurable bacterial genera analyzed or present in the stool sample.
  • a method of detecting an inflammatory disorder in a subject comprising the steps of quantifying or measuring the number of bacterial cells in a stool or fecal sample of said subject, determining the microbiome composition of said stool sample and detecting or measuring whether said stool sample is characterized by low cell count and by a high relative fraction of the Bacteroides genus and/or a lower relative fraction of the Faecalibacterium genus.
  • the order or sequence of the cell count measurements and the gut microbiome composition analysis is not important and will not influence the outcome of the diagnosis.
  • a method of detecting an inflammatory disorder in a subject comprising the steps of determining the microbiome composition or microbiome profile of a stool or fecal sample of said subject, quantifying the number of bacterial cells in said stool sample, and detecting whether said stool sample is characterized by a low cell count and by a high relative fraction of the Bacteroides genus and/or a low relative fraction of the Faecalibacterium genus.
  • said "low microbial cell count" is a cell count of less than 2x1o 11 , less than 1.5x1o 11 , less than lxlO 11 , less than 9xl0 10 , less than 8xl0 10 , less than 7xl0 10 , less than 6xl0 10 , less than 5xl0 10 , less than 4xl0 10 , less than 3xl0 10 , less than 2xl0 10 , less than 1.5xl0 10 , less than lxlO 10 , less than 9xl0 9 , less than 8xl0 9 , less than 7xl0 9 , less than 6xl0 9 , less than 5xl0 9 , less than 4xl0 9 , less than 3xl0 9 , less than 2xl0 9 , less than 1.5xl0 9 , less than lxlO 9 , less than 5x10 s or less than 5x10
  • said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus belongs to the most abundant genera in said stool sample of said subject. More particularly, said most abundant genera are the 100, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3 or 2 most abundant genera in said stool sample of said subject. Even more particularly, said most abundant genera are the 25%, 10%, 5%, 3%, 2% or 1% most abundant genera in said stool sample of said subject. In most particular embodiments of all the methods disclosed in this application, said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus is the most abundant genus in said stool sample of said subject.
  • said "high relative fraction of the Bacteroides genus” is an abundance of the Bacteroides genus which is at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 1.5 fold, at least 2 fold, at least 3 fold, at least 5 fold or at least 10 fold higher than the abundance of the other measured or detected bacterial genera in the stool sample of said subject.
  • Said abundance can be the average abundance of the other measured bacterial genera in said stool sample.
  • said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus belongs to the least abundant genera in said stool sample of said subject. More particularly, said least abundant genera are the 100, 50, 40 ,30 ,20, 10, 9, 8, 7, 6, 5, 4, 3 or 2 least abundant genera in said stool sample of said subject. Even more particularly, said least abundant genera are the 25%, 10%, 5%, 3%, 2% or 1% least abundant genera in said stool sample of said subject. In most particular embodiments of all the methods disclosed in this application, said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus is the least abundant genus in said stool sample of said subject.
  • said "low relative fraction of the Faecalibacterium genus" is an abundance of the Faecalibacterium genus which is at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 1.5 fold, at least 2 fold, at least 3 fold, at least 5 fold or at least 10 fold lower than the abundance of the other measured or detected bacterial genera in the stool sample of said subject.
  • Said abundance can be the average abundance of the other measured bacterial genera in said stool sample.
  • said "low microbial cell count” is a cell count of less than 1.5x1o 11 cells per gram stool
  • said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus belongs to the 10% most abundant genera in said stool sample of said subject
  • said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus belongs to the 25% least abundant genera in said stool sample of said subject.
  • said "low microbial cell count” is a cell count of less than 1.5x1o 11 cells per gram stool
  • said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus belongs to the 5% most abundant genera in said stool sample of said subject
  • said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus belongs to the 25% least abundant genera in said stool sample of said subject.
  • said "low microbial cell count” is a cell count of less than 1.5x1o 11 cells per gram stool
  • said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus belongs to the 10% most abundant genera in said stool sample of said subject
  • said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus belongs to the 5% least abundant genera in said stool sample of said subject.
  • said "low microbial cell count” is a cell count of less than 1.5x1o 11 cells per gram stool
  • said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus belongs to the 25% most abundant genera in said stool sample of said subject
  • said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus belongs to the 10% least abundant genera in said stool sample of said subject.
  • said "low microbial cell count” is a cell count of less than 1.5x1o 11 cells per gram stool
  • said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus belongs to the 25% most abundant genera in said stool sample of said subject
  • said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus belongs to the 5% least abundant genera in said stool sample of said subject.
  • said "low microbial cell count” is a cell count of less than 1.5x1o 11 cells per gram stool
  • said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus belongs to the 3% most abundant genera in said stool sample of said subject
  • said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus belongs to the 3% least abundant genera in said stool sample of said subject.
  • said "low microbial cell count” is a cell count of less than 1.5x1o 11 cells per gram stool
  • said "high relative fraction of the Bacteroides genus” means that the Bacteroides genus belongs to the 1% most abundant genera in said stool sample of said subject
  • said "low relative fraction of the Faecalibacterium genus” means that the Faecalibacterium genus belongs to the 1% least abundant genera in said stool sample of said subject.
  • the methods herein described can also be successfully used to diagnose a subject with one of the inflammatory disorders mentioned herein when the stool sample of the subject to be diagnosed is compared to a stool sample of a healthy person. Therefore, methods of detecting an inflammatory disorder in a subject are provided, said method comprising detecting whether a stool sample of said subject has a low microbial cell count and has a high relative fraction of the Bacteroides genus and/or a low relative fraction of the Faecalibacterium genus, wherein the microbial cell count and the abundance of the Bacteroides and/or Faecalibacterium genus are compared to a stool sample of a healthy subject.
  • said "low microbial cell count" is a microbial cell count which is at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 1.5 fold, at least 2 fold, at least 3 fold, at least 5 fold or at least 10 fold lower than the microbial count of a stool sample of a healthy subject.
  • said "high relative fraction of the Bacteroides genus" is an at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 1.5 fold, at least 2 fold, at least 3 fold, at least 5 fold or at least 10 fold higher relative abundance compared to the relative abundance of the Bacteroides genus in the stool sample of a healthy subject.
  • said "low relative fraction of the Faecalibacterium genus" is an at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 1.5 fold, at least 2 fold, at least 3 fold, at least 5 fold or at least 10 fold lower relative abundance compared to the relative abundance of the Faecalibacterium genus in the stool sample of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 2 and 5 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 1.5 and 3 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 1.5 and 3 fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 3 and 8 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 1.5 and 3 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 1.5 and 3 fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 1.5 and 6 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 1.5 and 3 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 2 and 5 fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 1.5 and 6 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 2 and 5 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 1.5 and 3 fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 1.5 and 6 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 2 and 5 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 2 and 5 fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 2 and 5 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 25% and 75% higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 2 and 5 fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 2 and 5 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 50% and 2 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 2 and 5 fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 2 and 5 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 25% and 75% higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 25% and 75% fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 2 and 5 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 50% and 2 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 25% and 75% fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 2 and 5 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 50% and 2 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 50% and 2 fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 2 and 5 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 2 and 5 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 25% and 75% fold lower than that of a healthy subject.
  • said low microbial cell count of a stool sample of a patient diagnosed with an inflammatory disorder is between 2 and 5 fold lower than that of a healthy subject and the high relative abundance of the Bacteroides genus between 2 and 5 fold higher than that of a healthy subject and/or the low relative abundance of the Faecalibacterium genus between 50% and 2 fold lower than that of a healthy subject.
  • method to detect an inflammatory disorder is equivalent to a “method to detect the presence or to assess the risk of development of an inflammatory disease”.
  • stool sample and "fecal sample” are used interchangeably and refer to as a sample or aliquot of the stool or feces of a subject, more particular a mammal, even more particularly a human being, most particularly a patient.
  • the stool sample as used herein comprises the gut microbiome from a human patient to be diagnosed.
  • microflora refers to the collective bacteria in an ecosystem of a host (e.g. an animal, such as a human) or in a single part of the host's body, e.g. the gut.
  • microbiota An equivalent term is "microbiota”.
  • microbiome refers to the totality of bacteria, their genetic elements (genomes) in a defined environment, e.g. within the gut of a host, the latter then being referred to as the "gut microbiome”.
  • Cell count refers to the sample cell density, in order words how many cells, more particularly microbial cells, are present in the sample, more particularly stool sample. Multiple methods are known by the skilled person to quantify microbial cell count in a stool sample, which is typically presented as cells per gram stool. Relative fraction” or “relative abundance” as used herein refers to the fraction or abundance of a certain genus with respect to or compared to a plurality of other genera present in the stool sample. "Bacteroides” as used herein refers to a genus of Gram-negative, obligate anaerobic bacteria. Bacteroides species are normally mutualistic, making up the most substantial portion of the mammalian gastrointestinal flora. The Bacteroides genus belongs to the family of Bacteroidaceae and a non-limiting example of a Bacteroides species is B. fragilis.
  • Faecalibacterium refers to a genus of bacteria of which its sole known species, Faecalibacterium prausnitzii is gram-positive, mesophilic, rod-shaped, anaerobic and is one of the most abundant and important commensal bacteria of the human gut microbiota. It is non-spore forming and non-motile. These Faecalibacterium bacteria produce butyrate and other short-chain fatty acids through the fermentation of dietary fiber.
  • RT-PCR real-time polymerase chain reaction
  • the term "patient” or “individual” or “subject” typically denotes humans, but may also encompass reference to non-human animals, preferably warm-blooded animals, more preferably mammals, such as, e.g. non-human primates, rodents, canines, felines, equines, ovines, porcines, and the like.
  • gut generally comprises the stomach, the colon, the small intestine, the large intestine, cecum and the rectum.
  • regions of the gut may be subdivided, e.g. the right versus the left side of the colon may have different microflora populations due to the time required for digesting material to move through the colon, and changes in its composition in time.
  • Synonyms of gut include the "gastrointestinal tract”, or possibly the “digestive system”, although the latter is generally also understood to comprise the mouth, esophagus, etc.
  • Quantifying cells such as microbial cells in a sample is a method well known for the skilled person. Examples for this can be found in Vandeputte et al 2017 (Nature 551: 507-511) which is incorporated as reference herein. Also the determination of the microbiome composition/profile to unravel the relative abundance of bacterial genera such as the Bacteroides and/or Faecalibacterium genus in a stool sample are standard methods especially in the field of gut microbiome analysis. In a particular embodiment, the microbiome composition or the microbiome profile in a stool sample is determined using microbiota phylogenetic profiling. The term "gut microbiome composition” is equivalent in wording as "gut microbiome profile” and these wordings are used interchangeably herein.
  • a gut microbiome profile represents the presence, absence or the abundance of one or more of bacterial genera identified in a stool sample.
  • the gut microbiome profile can be determined based on an analysis of amplification products of DNA and/or RNA of the gut microbiota, e.g. based on an analysis of amplification products of genes coding for one or more of small subunit rRNA, etc. and/or based on an analysis of proteins and/or metabolic products present in the biological sample.
  • Gut microbiome profiles may be "compared" by any of a variety of statistical analytic procedures.
  • Detection of microbial genera may be done in any of a number of ways that are known to those of ordinary skill in the art, including but not limited to culturing the organism or the few organisms, conducting various analyses which are indicative of the presence of the microbe(s) of interest (e.g. by microscopy, using staining techniques, enzyme assays, antibody assays, etc.), or by sequencing of genetic material (DNA or RNA), and others.
  • DNA or RNA DNA or RNA
  • nucleic acid(s) may be detected (usually amplified using, e.g. PCR techniques), particularly useful amplification strategies include the use of primers (e.g. universal primers) which amplify ribosomal RNA genes (rRNA).
  • primers e.g. universal primers
  • rRNA ribosomal RNA genes
  • 16S sequencing or “16S” refers to a sequence derived by characterizing the nucleotides that comprise the 16S ribosomal RNA gene(s).
  • the bacterial 16S rRNA is approximately 1500 nucleotides in length and is used in reconstructing the evolutionary relationships and sequence similarity of one bacterial isolated to another using phylogenetic approaches.
  • said phylogenetic profiling is performed using PCR, reverse transcriptase (RT)-PCR, quantitative (q)PCR, multiplex PCR, high- throughput sequencing, metatranscriptomic sequencing, identification of strain-specific markers and/or 16S rRNA analysis.
  • said microbiome profile of a subject is the subject's gut enterotype.
  • the types and/or the quantity (e.g. occurrence or abundance) in the sample of a multitude or plurality of bacteria, particularly of at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or 20 bacterial genera, more particularly of at least 30, 40 or 50 bacterial genera, even more particularly of at least 100 bacterial genera, most particularly of all detectable bacteria in the sample is determined according to any method known to those of skill in the art.
  • a total amount of bacteria may be determined, and then for each constituent bacteria, a fractional percentage (e.g. relative amount, ratio, distribution, frequency, percentage, etc.) of the total is calculated.
  • control results of the same parameter(s) obtained from asymptomatic individuals (negative control), and/or individuals known to have an inflammatory disorder (positive control), or from subjects who have had an inflammatory disorder and are being or have been treated, either successfully or unsuccessfully, etc.
  • what is determined is the abundance of microbial genera within the microbiome.
  • characterization may be carried to more detailed levels, e.g. to the level of species, and/or to the level of strain or variation (e.g. variants) within a species, if desired (including the presence or absence of various genetic elements such as genes, the presence or absence of plasmids, etc.).
  • higher taxonomic designations can be used such as Family, Phyla, Class, or Order.
  • the objective is to identify which bacteria are present in the sample from the individual and the relative abundance of those microbes, e.g. expressed as a percentage of the total number of microbes that are present, thereby establishing a microbiome profile or signature for the individual being tested, e.g. for the region of the gut that has been sampled, or for the type of sample that is analyzed.
  • the term "abundance” as used herein refers to the “amount” or “quantity” of a particular genus of gut microbiota.
  • the gut microbiome of a subject is profiled by detecting the bacterial genera in a stool sample of said subject. For example, but without the purpose of being limiting, this can be done by 16S rRNA sequencing.
  • the data retrieved by these profiling analyses can be modeled together with data retrieved from healthy subjects, preferably more than 50, more preferably more than 100, even more preferably more than 200, most preferably more than 500.
  • DMM Dirichlet- multinomial models
  • a method of detecting an inflammatory disorder in a subject comprising detecting whether a stool sample of said subject is characterized by a Bacteroides2 enterotype.
  • a method of detecting an inflammatory disorder in a subject comprising:
  • a method is provided to indicate a high probability of a patient or test subject developing or having an inflammatory disorder, said method comprising:
  • said Bacteroides2 enterotype is characterised by low microbial cell count and overrepresentation of the Bacteroides genus and/or underrepresentation of the Faecalibacterium genus.
  • said Bacteroides2 enterotype is characterised by a lower microbial cell count and a higher relative fraction of Bacteroides genus and/or lower relative fraction of Faecalibacterium genus compared to a Prevotella or Ruminococcaceae enterotype or to that of a stool sample of a healthy subject.
  • said Bacteroides2 enterotype is characterised by low microbial cell count, an overrepresentation of the Bacteroides genus and an underrepresentation of the Faecalibacterium genus.
  • said clustering is done using Dirichlet-multinomial models.
  • n is more than 100, more than 200, more than 300, more than 400, more than 500, more than 600, more than 700, more than 800, more than 900 or more than 1000.
  • said low microbial cell count is a cell count of less than 1.5x1o 11 or less than lxlO 11 cells per gram stool. In other particular embodiments said low microbial cell count is a microbial cell count which is at least 10%, at least 50%, at least 2 fold or at least 10 fold lower than the microbial count of a stool sample of a healthy subject.
  • a method is provided to indicate a high probability of a patient or test subject developing or having an inflammatory disorder, said method comprising:
  • a method to detect the presence or to assess the risk of development of an inflammatory disease in a patient comprising the steps of:
  • gut microbiome profile for said patient, said gut microbiome profile comprising an indication of the microbial cell count and of the relative fractions of at least 20 bacterial genera from a stool sample and comparing said gut microbiome profile of said patient to one or more gut microbiome reference profiles, wherein said one or more gut microbiome reference profiles comprise at least one of a positive gut microbiome reference profile based on results from control subjects with said inflammatory disease and a negative gut microbiome reference profile based on results from control subjects without said inflammatory disease,
  • said gut microbiome profile is determined using a stool sample of said patient and control subjects.
  • said at least 20 bacterial genera is at least 30 bacterial genera, at least 40 bacterial genera, at least 50 bacterial genera, at least 60 bacterial genera, at least 70 bacterial genera, at least 80 bacterial genera, at least 90 bacterial genera or at least 100 bacterial genera.
  • said bacterial genera are those that are most abundant in the stool sample of the patient and control subjects.
  • said gut microbiome profile comprises at least an indication of the relative fractions of Bacteroides genus and/or of Faecalibacterium genus.
  • a method is provided to detect the presence or to assess the risk of development of an inflammatory disease in a patient, comprising detecting whether the gut enterotype for said patient statistically significantly matches the Bacteroides2 enterotype.
  • methods are provided to detect the presence or to assess the risk of development of an inflammatory disease in a patient, comprising determining the gut enterotype for said patient, said gut enterotype consists of Bacteroidesl, Bacteroides2, Prevotella or Ruminococcaceae; wherein a Bacteroides2 enterotype is indicative for said patient to have or to be at risk of developing an inflammatory disease.
  • said gut enterotype is determined using a stool sample of said patient and control subjects.
  • a "positive control data” means a Bacteroides2 enterotype
  • negative control data means Bacteriodesl, Prevotella or Ruminococcaceae enterotype
  • the likelihood or risk of the patient for developing the disease or condition of interest is determined and thus can be used as a predictive diagnostic. For example, a person with a profile that is not similar to or within the range of values seen in control profiles which are not associated with disease, but which is more similar to or within ranges determined for disease associated profiles, may be deemed to be at high risk for developing the disease. This is generally the case, for example, if his/her microbiome profile, particularly his/her gut enterotype is associated with the disease state with a statistically significant (P value) of less than about 0.05 (after multiple testing correction).
  • P value statistically significant
  • the overall pattern of the gut microbiome is assessed, i.e. not only are particular genera identified, but the percentage of each constituent genus is taken in account, in comparison to all genera that are detected and, usually, or optionally, to each other.
  • the relationships may be expressed numerically or graphically as ratios or percentages of all genera detected, etc.
  • the data may be manipulated so that only selected subsets of the genera are considered (e.g. key indicators with strong positive correlations). Data may be expressed, e.g. as a percentage of the total number of microbes detected, or as a weight percentage, etc.
  • a nonparametric multivariate test such as Metastats, Analysis of Similarity, Principle Component Analysis, Non-Parametric MANOVA (Kruskal-Wallace), or other tests known by the skilled person, can be used to associate microbiome dysbiosis with a statistical significant (P value) of less than 0.05 (after multiple testing correction).
  • P value a statistical significant
  • Such tests are known in the art and are described, for example, by White et al., "Statistical methods for detecting differentially abundant features in clinical metagenomic samples", PLoS Comput Biol 5.4 (2009): el000352; Segata et al., “Metagenomic biomarker discovery and explanation", Genome Biol 12.6 (2011): R60.
  • phylogenetic methods such as Unifrac can be used to associate microbiome profiles with the disease state with a statistically significant (P value) of less than 0.05 (after multiple testing correction). See, for example, Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71:8228-8235.
  • Still other methods can be used to associate microbiome profiles with the disease state with a statistically significant (P value) of less than 0.05 (after multiple testing correction).
  • P value a statistically significant
  • Linear models MaAsLin - Tickle T, Waldron L, Yiren Lu, Huttenhower C. Multivariate association of microbial communities with rich metadata in high-dimensional studies, e.g. as in Morgan et al. Genome Biol 2015, 16:67
  • Machine Learning tools such as Random Forests, Support Vector Machines
  • ecological visualization tools (vegan: Community Ecology Package. R Package [Internet] 2015 J Oksanen, F Blanchet, R Kindt, P Legendre, P Minchin, R O'Hara), amongst others.
  • a method of diagnosing and treating an inflammatory disorder in a patient comprising the steps of:
  • a method of diagnosing and treating an inflammatory disorder in a patient comprising the steps of:
  • an “effective amount” of a composition is equivalent to the dosage of the composition that leads to treatment, prevention or a reduction of the severity of inflammation status in a patient.
  • Said inflammation can be gut inflammation for which several methods are known to the person skilled in the art to evaluate or thus to diagnose the severity of the inflammation.
  • a method of diagnosing and treating an inflammatory disorder in a patient comprising the steps of:
  • a method of diagnosing and treating an inflammatory disorder in a patient comprising the steps of:
  • said stool sample comprises the gut microbiome from a human patient to be diagnosed or from a healthy control.
  • a method of diagnosing and treating an inflammatory disease in a patient comprising the steps of:
  • gut microbiome profile for said patient, said gut microbiome profile comprising an indication of the microbial cell count and of the relative fractions of at least 20 bacterial genera from a stool sample and comparing said gut microbiome profile of said patient to one or more gut microbiome reference profiles, wherein said one or more gut microbiome reference profiles comprise at least one of a positive gut microbiome reference profile based on results from control subjects with said inflammatory disease and a negative gut microbiome reference profile based on results from control subjects without said inflammatory disease,
  • said gut microbiome profile is determined using a stool sample of said patient and control subjects.
  • said at least 20 bacterial genera is at least 30 bacterial genera, at least 40 bacterial genera, at least 50 bacterial genera, at least 60 bacterial genera, at least 70 bacterial genera, at least 80 bacterial genera, at least 90 bacterial genera or at least 100 bacterial genera.
  • said bacterial genera are those that are most abundant in the stool sample of the patient and control subjects.
  • said at least 20 bacterial genera comprises the Bacteroides genus and/or the Faecalibacterium genus.
  • a method is provided of diagnosing and treating an inflammatory disease in a patient, comprising administering anti-inflammatory therapy to said patient if the gut enterotype for said patient statistically significantly matches the Bacteroides2 enterotype.
  • methods are provided to diagnose and treat an inflammatory disease in a patient, comprising determining the gut enterotype for said patient, said gut enterotype consists of Bacteroidesl, Bacteroides2, Prevotella or Ruminococcaceae; and administering anti-inflammatory therapy to said patient if said gut enterotype of said patient is a Bacteroides2 enterotype.
  • said gut enterotype is determined using a stool sample of said patient and control subjects.
  • Commonly used anti-inflammatory drugs are inhibitors of cyclooxygenase activity (aspirin, celecoxib, diclofenac, diflunisal, etodolac, ibuprofen, indomethacin, ketoprofen, ketorolac, meloxicam, nabumetone, naproxen, oxaprozin, piroxicam, salsalate, sulindac, tolmetin, among others) or corticosteroids (prednisone, dexamethasone, hydrocortisone, methylprednisolone, among others) or in combination with commonly used analgesics (acetaminophen, duloxetine, paracetamol, among others) or in any combination thereof.
  • analgesics acetaminophen, duloxetine, paracetamol, among others
  • said anti-inflammatory therapy includes a biological therapy, such as TNF-alpha blockers, anti-IL17A monoclonal antibodies, anti-CD20 antibodies.
  • a biological therapy such as TNF-alpha blockers, anti-IL17A monoclonal antibodies, anti-CD20 antibodies.
  • the therapeutic options for CD or UC include corticosteroids, aminosalicylates, immunosuppressive agents and biological therapies. Due to the chronic relapsing and remitting disease-course of IBD, the goal of medical therapy is to induce (induction phase) and maintain remission (maintenance phase).
  • inflammation refers to complex but to the skilled person well known biological response of body tissues to harmful stimuli, such as pathogens, damaged cells, or irritants.
  • inflammation is not a synonym for infection. Infection describes the interaction between the action of microbial invasion and the reaction of the body's inflammatory response—the two components are considered together when discussing an infection, and the word is used to imply a microbial invasive cause for the observed inflammatory reaction. Inflammation on the other hand describes purely the body's immunovascular response, whatever the cause may be. Inflammation is a protective response involving immune cells, blood vessels, and molecular mediators.
  • inflammation The function of inflammation is to eliminate the initial cause of cell injury, clear out necrotic cells and tissues damaged from the original insult and the inflammatory process, and to initiate tissue repair.
  • the classical signs of inflammation are heat, pain, redness, swelling, and loss of function.
  • Inflammation is a generic response, and therefore it is considered as a mechanism of innate immunity, as compared to adaptive immunity, which is specific for each pathogen. Inflammation can be classified as either acute or chronic.
  • Acute inflammation is the initial response of the body to harmful stimuli and is achieved by the increased movement of plasma and leukocytes (especially granulocytes) from the blood into the injured tissues.
  • a series of biochemical events propagates and matures the inflammatory response, involving the local vascular system, the immune system, and various cells within the injured tissue.
  • Prolonged inflammation known as chronic inflammation, leads to a progressive shift in the type of cells present at the site of inflammation, such as mononuclear cells, and is characterized by simultaneous destruction and healing of the tissue from the inflammatory process.
  • said inflammation is associated with at least one of the following disorders or conditions: spondyloarthritis, ankylosing spondylitis, reactive arthritis, psoriatic arthritis, enteropathic arthritis, undifferentiated spondyloarthritis, juvenile idiopathic arthritis, primary sclerosing cholangitis, multiple sclerosis, inflammatory bowel disease, Crohn's disease, ulcerative colitis and any combination thereof.
  • said inflammatory disorder is a gut inflammatory disorder while said inflammation is gut inflammation.
  • gut inflammation is equivalent to the wording "microscopic gut inflammation” as used herein and refers to an inflammatory response in the gut as defined above.
  • the inflammation can affect the entire gastrointestinal tract, can be more limited to for example the small intestine or large intestine but can also be limited to specific components or structures such as the bowel walls.
  • said gut inflammatory disorder or said gut inflammation is selected from the list consisting of Crohn's disease, irritable bowel syndrome, inflammatory bowel disease (IBD), ulcerative colitis, celiac disease, primary sclerosing cholangitis, multiple sclerosis, spondyloarthritis and gut inflammation associated with primary sclerosing cholangitis, multiple sclerosis or spondyloarthritis.
  • IBD inflammatory bowel disease
  • IBD inflammatory bowel disease
  • the term "inflammatory bowel disease” or abbreviated “IBD” refers to an umbrella term for inflammatory conditions of the gut under which both Crohn's disease and ulcerative colitis fall.
  • IBD inflammatory bowel disease
  • the immune system mistakes food, bacteria, or other materials in the gut for foreign substances and responds by sending white blood cells into the lining of the bowels.
  • the result of the immune system's attack is chronic inflammation.
  • Crohn's disease and ulcerative colitis are the most common forms of IBD. Less common IBDs include microscopic colitis, diverticulosis-associated colitis, collagenous colitis, lymphocytic colitis and Behget’s disease.
  • transmural inflammation commonly affects the terminal ileum, although any part of the gastrointestinal system can be affected.
  • Discontinuous inflammation and the presence of non-caveating granulomas are also characteristic of the inflammation in patients with CD.
  • UC is characterized by continuous mucosal inflammation starting in the rectum and extending proximally until the caecum (Harries et al 1982 Br Med J Clin Res Ed, 284:706).
  • These are chronic relapsing diseases originating mostly during adolescence and young adulthood and are characterized by chronic inflammation of the gastrointestinal tract leading to invalidating symptoms of bloody diarrhea, weight loss and fatigue (Wilks 1859 Med Times Gazette 2:264- 265).
  • T helper lymphocytes are cytokine producing lymphocytes that potentiate or regulate immune responses by interacting with other immune cells such as macrophages, CD8+ T cells, eosinophils and basophils. Following an initial trigger (e.g.
  • the microbe-associated molecular patterns will induce the secretion of cytokines by dendritic cells, epithelial cells and macrophages, among others.
  • Different cytokine milieus will induce TH1, TH2, TH17 or regulatory T-cell (Treg) subsets (de Souza et al 2016 Nat Rev Gastroenterol Hepatol 13:13-27).
  • Treg regulatory T-cell
  • UC has been described as a TH2-like condition with possible implication of a newly discovered TH9 lymphocytes (de Souza et al 2016 Nat Rev Gastroenterol Hepatol 13:13-27; Gerlach et al 2014 Nat Immunol 15:676-686).
  • an insufficient Treg response seems to be involved in the impaired regulation of inflammatory responses (Maul et al 2005 Gastroenterology 128:1868-1878).
  • active IBD the immune system shows an increased response to bacterial stimulation, thereby contributing even further to the chronic inflammatory state. This inflammatory state also produces an increase in the intestinal permeability, allowing bacterial antigens to contact with the immune system, hereby perpetuating the inflammatory state.
  • said inflammation or inflammatory disorder as used in the methods of current application is inflammation or an inflammatory disorder characterized by a TH1, TH17, TH2 and/or TH9 response.
  • said inflammation or inflammatory disorder is characterized by a TH1 and/or TH17 response.
  • SpA spondyloarthritis
  • This SpA group is also sometimes referred to as spondylitis and spondyloarthropathies.
  • SpA includes ankylosing spondylitis (including non-radiographic axial SpA, i.e.
  • SpA ankylosing spondylitis diagnosed using MRI
  • reactive arthritis psoriatic arthritis
  • enteropathic arthritis arthritis associated with inflammatory bowel disease or IBD related arthritis
  • undifferentiated spondyloarthritis juvenile idiopathic arthritis
  • juvenile-onset SpA Characteristics of these SpA diseases include inflammatory arthritis of the spine, peripheral arthritis that differs from rheumatoid arthritis, extra articular manifestations of inflammatory bowel disease, arthritis and uveitis, seronegativity for rheumatoid factor and some degree of heritability, including the presence of the gene HLA-B27. It is thus clear that in current application SpA is not rheumatoid arthritis.
  • Primary sclerosing cholangitis or "PSC” as used herein refers to a severe chronic liver disease characterized by progressive biliary inflammation and fibrosis.
  • PSC Primary sclerosing cholangitis
  • Patients with PSC are usually asymptomatic and the diagnostic work up is triggered by incidental findings of altered liver enzymes.
  • fatigue, pruritus, abdominal pain and jaundice are the most reported symptoms (Lazaridis et al 2016 N Engl J Med 375:1161-1170).
  • magnetic resonance cholangiography or endoscopic retrograde cholangiopancreatography are used to establish the diagnosis.
  • liver biopsy is reserved to diagnose suspected small duct PSC or to exclude other diagnosis (Lindor et al 2015 Am J Gastroenterol 110:646-659). It would thus be highly advantageous to develop presymptomatic diagnostic methods or non-invasive diagnostic methods.
  • the diagnostic methods disclosed above solve this technical problem. Therefore, in a particular embodiment, all method disclosed in this application are method of diagnosing primary sclerosing cholangitis, more particularly gut inflammation associated with primary sclerosing cholangitis.
  • the inflammatory disorder as mentioned in the application refers to inflammatory disorders characterized by a TH17 response.
  • MS Multiple sclerosis
  • CNS central nervous system
  • RR relapsing-remitting
  • SP secondary progressive
  • PP primary progressive
  • mice raised in a germ-free environment were highly resistant to developing spontaneous EAE, unless exposed to specific pathogen- free condition-derived fecal material or a fecal transplant from MS twin-derived microbiota (Berer K et al 2011 Nature 479:538-541; Berer et al 2017 Proc Nat Ac Sc USA).
  • Immune cells from mouse recipients of MS-twin samples produced less IL-10 than immune cells from mice colonized with healthy-twin samples.
  • IL-10 may have a regulatory role in spontaneous CNS autoimmunity, as neutralization of the cytokine in mice colonized with healthy-twin fecal samples increased disease incidence. This evidence suggests that the microbiota may be capable of altering the individual at a phenotypic level and influence the onset, severity and progression of MS. Therefore, in a particular embodiment, the methods disclosed above are methods of detecting multiple sclerosis or gut inflammation associated with multiple sclerosis.
  • the stool sample of said subject to be diagnosed for an inflammatory disease is further characterized by a lower bacterial diversity compared to a stool sample of a healthy subject. More particularly, said stool sample is even further characterized by the absence of Escherichia coli and/or the Prevotella.
  • Example 1 Disease-associated dysbiosis is captured by enterotyping
  • Example 2 Intestinal inflammation is associated with reduced fecal cell counts
  • MS Multiple sclerosis
  • CNS central nervous system
  • MS patients diagnosed according to the 2010 revised McDonald criteria (Polman et al 2011 Ann Neurol 69:292-302), were recruited from the National MS center Melsbroek and University Hospital Brussels, Belgium. Inclusion was based on the clinical phenotype and well-defined disease characteristics, considering presentation at onset, Expanded Disability Status Scale (EDSS), disease activity and treatment status.
  • EDSS Expanded Disability Status Scale
  • the RRMS group consisted of untreated active RRMS, untreated active RRMS during relapse and interferon (IFN) treated RRMS.
  • IFN interferon
  • Flow cytometric measurements were done using FL1 and FL3 channels for primary gating of the microbial population. Afterwards, FSC and SSC channels were used to gate out background events that were still present to determine accurate cell counts. The cell counts were converted to microbial loads per gram of fecal material (fresh weight).
  • DNA extraction and sequencing data pre-processing Fecal DNA extraction and microbiota profiling was performed as described previously (Sabino et al 2016 Gut 65:1681-1689). Briefly, DNA was extracted from fecal material using the MoBio PowerMicrobiome RNA isolation kit. The V4 region of the 16S rRNA gene was amplified with primer pair 515F/806R as described by Kozich et al. (2013 Appl Environ Microbiol 79:5112-5120). Sequencing was performed on the lllumina MiSeq platform (San Diego, California, USA), to generate paired-end reads of 250 bases in length in each direction.
  • RMP Relative microbiome profiles
  • the microbial flow cytometric measurements were used to transform sequencing data into an absolute genus abundance matrix.
  • This methodology has three essential steps. Firstly, the reads that are classified at genus level are corrected for copy number variation, using RDP classifier 2.12 (Wang et al 2007 Appl Environ Microbiol 73:5261-5267). Secondly, samples were downsized to even sampling depth, defined as the ratio between sample size (16S rRNA gene copy number corrected sequencing depth) and microbial load (total cell counts per gram of fecal material (fresh weight)). Each sample was rarefied to the number of reads equivalent to the minimum observed sampling depth in the cohort that allowed to preserve at least 150 reads per sample. From the total 160 samples, only 27 samples are below 500 reads per sample. Hereby, some samples were excluded from the analysis. Thirdly, the obtained genus abundances within samples are made proportional and are then multiplied by the total microbial load per sample.
  • Fecal calprotectin was measured with fCAL ELISA kit (Buhlmann, Schonenbuch, Switzerland), both in fresh and in frozen samples. Patients with PSC and patients with CD were asked to provide fresh fecal samples, which were frozen at -80°C within 12 hours after sampling. Fecal calprotectin was measured in fresh aliquots from these samples. Samples from patients with UC were already available in our biobank, therefore, fecal calprotectin was measured in frozen aliquots from these samples. The FGFP volunteers were asked to provide a frozen fecal sample. Frozen aliquots from the FGFP samples were used for fecal calprotectin measurements.
  • R software (version 3.4.1) was used for statistical analysis. Parametric and non-parametric tests were used when applicable. Shapiro-Wilk test was used to test for normality. Numerical ecology statistics were facilitated by the phyloseq (version 1.20.0) and vegan (version 2.4-3) R packages. Mann-Whitney U was used to test median differences of genera abundances between different groups. Non-parametric Kruskal-Wallis test with post-hoc analysis with Dunn test were used to test differences between more than two groups. Parametric ANOVA with post-hoc Tukey test were used when ANOVA assumptions were met. Spearman correlation was used for correlations between two continuous variables. Comparison between two or more categorical variables was performed with Pearson's Chisquared test, followed by post-hoc pairwise Fisher tests when appropriate. All p-values were corrected for multiple testing when appropriate, the method (FDR or Bonferroni) used is always reported alongside the p values.

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Abstract

La présente invention concerne le domaine des troubles ou des états associés à l'inflammation, plus particulièrement l'inflammation intestinale. L'invention concerne des moyens et des procédés pour diagnostiquer et traiter ou réduire la gravité de troubles ou d'états associés à une inflammation chez un sujet en ayant besoin.
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